A grid-point detection method based on U-net for a structured light system
This method improves the accuracy of grid-point detection for structured light 3D reconstruction systems, which is beneficial for applications requiring precise one-shot 3D measurements.
This paper proposes a U-net based method for detecting grid points in structured light systems, which are crucial for one-shot 3D reconstruction. The method achieves higher accuracy compared to previous approaches.
Accurate detection of the feature points of the projected pattern plays an extremely important role in one-shot 3D reconstruction systems, especially for the ones using a grid pattern. To solve this problem, this paper proposes a grid-point detection method based on U-net. A specific dataset is designed that includes the images captured with the two-shot imaging method and the ones acquired with the one-shot imaging method. Among them, the images in the first group after labeled as the ground truth images and the images captured at the same pose with the one-shot method are cut into small patches with the size of 64x64 pixels then feed to the training set. The remaining of the images in the second group is the test set. The experimental results show that our method can achieve a better detecting performance with higher accuracy in comparison with the previous methods.